Cognitive automation-based engine to propagate data across systems

ABSTRACT

Aspects of the disclosure relate to cognitive automation-based engine processing to propagate data across multiple systems via a private network to overcome technical system, resource consumption, and architecture limitations. Data to be propagated can be manually input or extracted from a digital file. The data can be parsed by analyzing for correct syntax, normalized into first through sixth normal forms, segmented into packets for efficient data transmission, validated to ensure that the data satisfies defined formats and input criteria, and distributed into a plurality of data stores coupled to the private network, thereby propagating data without repetitive manual entry. The data may also be enriched by, for example, correcting for any errors or linking with other potentially related data. Based on data enrichment, recommendations of additional target(s) for propagation of data can be identified. Reports may also be generated. The cognitive automation may be performed in real-time to expedite processing.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is continuation of U.S. application Ser. No. 16/808,856filed Mar. 4, 2020, entitled, “COGNITIVE AUTOMATION-BASED ENGINE TOPROPAGATE DATA ACROSS SYSTEMS” which is incorporated herein by referencein their entirety.

TECHNICAL FIELD OF DISCLOSURE

Aspects of the disclosure relate to processes, machines, and platformsfor data processing, artificial intelligence and, in particular, toknowledge processing of collections of facts, data, information, andrelationships to which reasoning techniques are applied in order topropagate data across multiple systems and platforms, and to avoidand/or minimize repetitive data entry.

BACKGROUND

Company employees often spend many hours each week performing repetitivedata entry across multiple systems. These repetitive tasks wastecomputational resources, network resources, hours of employee time,business resources, and company money. Further, repetitive entry of datainto multiple systems is a very tedious task, affects productivity, andis prone to manual errors, which may result in data inconsistency. Italso presents various technical problems such as an inability ofcomputer systems to automatically reconcile conflicting data entries aswell increased computer resource and bandwidth consumption attempting toexhaustively search for, identify, and access disparate systems, datastores, and data warehouses to find conflicting information, filter datain the systems to identify portions that need to be analyzed andupdating of each source or repository of data, etc.

An example of this is with respect to a company's sales orders. Salesrepresentatives often need to spend their critical time in entering datato both customer relationship management (CRM) and enterprise resourceplanning (ERP) systems. CRM helps track and manage customerrelationships. For example, CRM includes software applications andsystems used by customer service representatives to manage customerinteractions, document customer service processes, and implement acustomer-centric philosophy and methodology. ERP systems often integrateengineering, planning, materials management, finance and humanresources. ERP systems integrate data systems with manufacturingprocesses to help speed raw material supplies and work with every aspectof an organization to ensure efficient operation.

In this sales order example, employees may have to enter various typesof information for every sale into a CRM system, an ERP system, andpotentially other systems for every single sale. Consistent and accurateinformation across all of the systems is crucial in order to managecustomer relationships and plan enterprises resources. Sample types ofinformation may include customer names, sales orders, purchase orders,invoicing, products ordered, supply sourcing for manufacturing thatcorresponds to the orders, inventory, date event tracking, calendarscheduling, sales personnel, customer service personnel, call logs, etc.Such prior art methods of manually entering all of this data intomultiple systems is problematic for the foregoing reasons.

This disclosure addresses one or more of the shortcomings in theindustry to overcome the technical problems associated with repetitivedata entry and propagating data across multiple systems.

SUMMARY

Aspects of the disclosure provide effective, efficient, scalable, andconvenient technical solutions that address and overcome the technicalproblems associated with repetitive data entry and propagation of dataacross multiple systems by utilizing cognitive automation in inventiveprocesses, machines, and platforms.

In some embodiments, a cognitive automation-based platform forpropagating data across multiple systems via a private network can beused to avoid, inter alia, slowing down enterprise computing resourcesand networks as a result of repetitive data entry by a user. Theplatform may be implemented in the form of a client-server architecture.The client computing machine can have: at least one client processor; atleast one client communication interface communicatively coupled to theat least one client processor and the private network; a client monitorcommunicatively coupled to the at least one client communicationinterface; a client keyboard communicatively coupled to the at least oneclient communication interface; and client memory communicativelycoupled to the at least one client communication interface, said clientmemory storing client computer-readable instructions that, when executedby the at least one client processor, cause the client computing deviceto perform various functions. The at least one client processor canreceive an input source file from a data source. The input source datacan be stored in a first sector of the client memory. The input sourcedata to be propagated can be retrieved from the first client sector ofthe client memory. The input source data can be transmitted, inreal-time if desired, from the client computing device to the servercomputing device.

The server can have: at least one server processor; at least one servercommunication interface communicatively coupled to the at least oneserver processor and the private network; and server memorycommunicatively coupled to the server communication interface. Theclient memory can store server computer-readable instructions that, whenexecuted by the at least one server processor, cause the servercomputing device to perform various functions. The input source datathat was transmitted can be received from the client computing device.The input source data can be stored in a first server sector of theserver memory.

The at least one server processor can parse the input source data intoparsed data by analyzing the input source data for correct syntax. Theparsed data can be stored by at least one server processor in a secondserver sector of the server memory. The parsed data can be normalized bythe at least one server processor into normalized data by breaking theparsing data into record groups for efficient processing. The normalizeddata can be normalized by the at least one server processor into atleast a first normal form. If desired, the normalized data can befurther normalized by the at least one server processor into one or moreof a second normal form, a third normal form, a fourth normal form, afifth normal form, and/or a sixth normal form. The normalized data canbe stored by the at least one server processor in a third server sectorof the server memory. The normalized data can be segmented by the atleast one server processor into segmented data by breaking thenormalized data into a plurality of smaller packets for efficient datatransmission. The at least one server processor can store, in a fourthserver sector of the server memory, the segmented data. The at least oneserver processor can validate the segmented data into validated data toensure that the validated data satisfies defined formats and inputcriteria. The at least one server processor can store, in a fifth serversector of the server memory, the validated data. The at least one serverprocessor can distribute, by the at least one server processor over theprivate network, the validated data into a plurality of data storescommunicatively coupled to the private network, thereby propagating saidvalidated data without said repetitive data entry by the user. The atleast one server processor can enrich the validated data to enricheddata that is corrected for any errors. The at least one server processorcan store, in a sixth server sector of the server memory, the enricheddata. The at least one server processor can generate a recommendation ofat least one additional target in which the enriched data may be stored.The at least one server processor can store, in a seventh server sectorof the server memory, the recommendation of said at least one additionaltarget in which the enriched data may be stored. The at least one serverprocessor can transmit to the client server processor on the clientcomputing machine over the private network, the recommendation fordisplay in a first graphical user interface on the client monitor. Theat least one server processor can store the enriched data in the atleast one additional target if approved by the user.

In some embodiments, the input source data can be a digital file. Inaddition, the digital file may comprise structured and/or unstructureddata.

In some embodiments, the server computing machine may perform one ormore steps or one or more functions in real-time. These functionsincluding one or more of parsing, normalizing the parsed data,segmenting the normalized data, validating the segmented data,distributing the validated data to a plurality of data stores, enrichingthe validated data, and generating recommendations to a user ofpotential additional target systems or target locations where the datamay be optionally propagated.

In some embodiments, the data source may be populated based on a manualentry of information by the user via the client keyboard in response toa second graphical user interface displayed on the client monitor.

In some embodiments, a report or log may be generated to report on oneor more aspects of the cognitive automation process or on thepropagation of data to multiple systems.

In some embodiments, one or more non-transitory computer-readable mediacan store instructions that, when executed by a cognitive automationcomputing platform comprising a plurality of processors in aclient-server architecture communicatively coupled over a privatenetwork via a plurality of communication interfaces, may provide thecognitive automation-based functionality of the present disclosure. Afirst of said plurality of processors may receive, from a data source,input source data. The first of said plurality of processors may store,in a first sector of the computer-readable media, the input source data.The first of said plurality of processors may extract (in real-time ifdesired), from the input source data in the first sector of thecomputer-readable media, data to be propagated. The first of saidplurality of processors may store (in real-time if desired), in a secondsector of the computer-readable media, the data to be propagated. Thefirst of said plurality of processors may transmit (in real-time ifdesired) the data to be propagated to a second of said plurality ofprocessors, which would receive the data to be propagated (again, inreal-time if desired).

The second of said plurality of processors may store (in real-time ifdesired), in a third sector of the computer-readable media, the data tobe propagated. The second of said plurality of processors may parse (inreal-time if desired) the data to be propagated into parsed data byanalyzing the data to be propagated for correct syntax. The second ofsaid plurality of processors may store (in real-time if desired), in afourth sector of the computer-readable media, the parsed data. Thesecond of said plurality of processors may normalize (in real-time ifdesired) the parsed data into normalized data by breaking the parsingdata into record groups for efficient processing. The normalized datacan be normalized into at least a first normal form or furthernormalized into forms two through sixth. The second of said plurality ofprocessors may store (in real-time if desired), in a fifth sector of thecomputer-readable media, the normalized data. The second of saidplurality of processors may segment (in real-time if desired) thenormalized data into segmented data by breaking the normalized data intoa plurality of smaller packets for efficient data transmission. Thesecond of said plurality of processors may store (in real-time ifdesired), in a sixth sector of the computer-readable media, thesegmented data. The second of said plurality of processors may validate(in real-time if desired) the segmented data into validated data toensure that the validated data satisfies defined formats and inputcriteria. The second of said plurality of processors may store (inreal-time if desired), in a seventh sector of the computer-readablemedia, the validated data. The second of said plurality of processorsmay distribute (in real-time if desired), over the private network, thevalidated data into a plurality of data stores communicatively coupledto the private network, thereby propagating said validated data withoutrepetitive data entry. The second of said plurality of processors mayenrich (in real-time if desired) the validated data to enriched datathat is corrected for any errors. The second of said plurality ofprocessors may store (in real-time if desired), in an eighth sector ofthe computer-readable media, the enriched data. The second of saidplurality of processors may generate (in real-time if desired) arecommendation of at least one additional target in which the enricheddata may be stored. The second of said plurality of processors may store(in real-time if desired), in a ninth sector of the computer-readablemedia, the recommendation of said at least one additional target inwhich the enriched data may be stored. The second of said plurality ofprocessors may transmit (in real-time if desired) to the first of saidplurality of processors, over the private network, the recommendation.The second of said plurality of processors may store the enriched datain the at least one additional target if approved.

In some embodiments, a cognitive automation-based method for propagatingdata across multiple systems via a private network to avoid repetitivedata entry by a user can be implemented. The method may comprise one ormore steps such as: receiving, by a cognitive automation platform from adata source, input source data; storing, by the cognitive automationplatform in a first sector of computer-readable media, the input sourcedata; extracting (in real-time if desired), by the cognitive automationplatform from the input source data in the first sector of thecomputer-readable media, data to be propagated; storing (in real-time ifdesired), by the cognitive automation platform in a second sector of thecomputer-readable media, the data to be propagated; parsing (inreal-time if desired), by the cognitive automation platform, the data tobe propagated into parsed data by analyzing the data to be propagatedfor correct syntax; storing (in real-time if desired), by the cognitiveautomation platform in a fourth sector of the computer-readable media,the parsed data; normalizing (in real-time if desired), by the cognitiveautomation platform, the parsed data into normalized data by breakingthe parsing data into record groups for efficient processing, saidnormalized data normalized into at least a first normal form oroptionally further into forms two through sixth; storing (in real-timeif desired), by the cognitive automation platform in a fifth sector ofthe computer-readable media, the normalized data; and segmenting (inreal-time if desired), by the cognitive automation platform, thenormalized data into segmented data by breaking the normalized data intoa plurality of smaller packets for efficient data transmission; storing(in real-time if desired), by the cognitive automation platform in asixth sector of the computer-readable media, the segmented data.

The method may further comprise validating (in real-time if desired), bythe cognitive automation platform, the segmented data into validateddata to ensure that the validated data satisfies defined formats andinput criteria; storing (in real-time if desired), by the cognitiveautomation platform in a seventh sector of the computer-readable media,the validated data; distributing (in real-time if desired), by thecognitive automation platform over the private network, the validateddata into a plurality of data stores communicatively coupled to theprivate network, thereby propagating said validated data withoutrepetitive data entry; enriching (in real-time if desired), thecognitive automation platform, the validated data to enriched data thatis corrected for any errors; storing (in real-time if desired), by thecognitive automation platform in an eighth sector of thecomputer-readable media, the enriched data; generating (in real-time ifdesired), by the cognitive automation platform, a recommendation of atleast one additional target in which the enriched data may be stored;storing (in real-time if desired), by the cognitive automation platformin a ninth sector of the computer-readable media, the recommendation ofsaid at least one additional target in which the enriched data may bestored; transmitting (in real-time if desired), by the cognitiveautomation platform over the private network, the recommendation; andstoring, by the cognitive automation platform, the enriched data in theat least one additional target if approved by a user.

In some embodiments, the input source data can be generated, by serveror other processor(s), by scanning a digital file.

In some embodiments, the input source data can be generated, by serveror other processor(s), by performing optical character recognition onthe digital file.

In some embodiments, the input source data can be generated, by serveror other processor(s), by forecast validation based on prognostic outputfrom at least one numerical model.

In some embodiments, the input source data can be generated, by serveror other processor(s), by regression validation by determining whetheran output of a regression model is adequate.

In some embodiments, the input source data can be generated, by serveror other processor(s), by social validation to verify compliance in asocial activity.

In some embodiments, the input source data can be generated, by serveror other processor(s), by statistical model validation to determine withoutputs of a statistical model are acceptable.

In some embodiments, the input source data can be generated, by serveror other processor(s), by documenting that a process meets predeterminedspecifications and fulfills an intended purpose.

In some embodiments, the input source data can be generated, by serveror other processor(s), by checking whether segmented data follows adefined structure.

In some embodiments, some or all server computer-readable instructionsare performed in real-time.

These features, along with many others, are discussed in greater detailbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIGS. 1 and 2 depict illustrative computing environment(s),client-server configurations, cognitive automation computer machines,platform(s), and/or module(s), in accordance with one or moreenvironments, for processing data from disparate sources and propagatingthe data to target applications and data stores;

FIG. 3 depicts sample contents of data sources and/or data stores, inaccordance with one or more environments, for processing data fromdisparate sources and propagating the data to target applications anddata stores;

FIG. 4 depicts a sample functional overview of and informationpropagation between data sources/data stores (or applications containedtherein) and cognitive automation computer machines, platforms, and/ormodules, in accordance with one or more environments, for processingdata from disparate sources and propagating the data to targetapplications and data stores; and

FIG. 5 depicts sample high-level representation(s) of the flow ofalgorithm(s) to implement various aspects of the cognitive automationprocessing and propagation of data, in accordance with one or moreembodiments.

DETAILED DESCRIPTION

In the following description of various illustrative embodiments,reference is made to the accompanying drawings, which form a parthereof, and in which is shown, by way of illustration, variousembodiments in which aspects of the disclosure may be practiced. It isto be understood that other embodiments may be utilized, and structuraland functional modifications may be made, without departing from thescope of the present disclosure.

It is noted that various connections between elements are discussed inthe following description. It is noted that these connections aregeneral and, unless specified otherwise, may be direct or indirect,wired or wireless, and that the specification is not intended to belimiting in this respect.

As used throughout this disclosure, computer-executable “software anddata” can include one or more: algorithms, applications, applicationprogram interfaces (APIs), attachments, big data, daemons, emails,encryptions, databases and data structures (including cubes, hypercubes,data warehouses, multidimensional databases, multidimensional databasemanagement systems, multidimensional data structures, online analyticalprocessing (OLAP) applications, cubes and data storage, relationaldatabases, etc.), datasets, data sources, drivers, file systems ordistributed file systems, firmware, graphical user interfaces, images,instructions, machine learning, middleware, modules, objects, operatingsystems, processes, protocols, programs, scripts, tools, and utilities.The computer-executable software and data is on tangible,computer-readable memory (local, in network-attached storage, remote,and/or online), can be stored in volatile or non-volatile memory, andcan operate automatically and/or autonomously, based on event triggers,on-demand, on a schedule, and/or as part of batch processing. It canoperate in real time or otherwise.

“Computer machine(s)” and “cognitive automation machine(s) and/orplatform(s”) can include one or more: special-purpose network-accessibleadministrative computers, clusters, computing devices or computingmachine(s), computing platforms, desktop computers, distributed systems,enterprise computers, laptop or notebook computers, controllercomputers, nodes, personal computers, portable electronic devices,servers, controlled computers, smart devices, tablets, and/orworkstations, which have one or more microprocessors, cores, and/orexecutors such as for executing or accessing the computer-executablesoftware and data. References to computer machines and names of devicesincluded within this definition are used interchangeably in thisspecification and are not considered to be limiting or exclusive to onlya specific type of device or type of user. Instead, references in thisdisclosure to computer machines, platforms, and the like are to beinterpreted broadly as understood by skilled artisans. Further, as usedin this specification, computer machines also include all hardware andcomponents typically contained therein such as, for example,processors/executors/cores 111, volatile and non-volatile memories 112,modules in memory 112 a-112 u, communication interfaces 113, monitor(s)118, and keyboards (not shown) or other input devices (not shown) etc.

Volatile and non-volatile memories may be comprised of one or morecomputer-readable media containing a plurality of sectors. As usedherein, a “sector” is broadly defined as subdivision(s) or block(s) ofmemory and is not limited to the minimum storage unit of a hard drive orother computer-readable medium. Further, the sector may have a fixedsize or may be variable.

Computer “networks” can include one or more local area networks (LANs),wide area networks (WANs), the Internet and public networks 180,wireless networks, digital subscriber line (DSL) networks, frame relaynetworks, asynchronous transfer mode (ATM) networks, private networks170, virtual private networks (VPN), or any combination of any of thesame. Networks also include associated “network equipment” such asaccess points, ethernet adaptors (physical and wireless), firewall(s)175, hubs, modems, routers, security devices, and/or switches locatedinside the network and/or on its periphery, as well as softwareexecuting on any of the foregoing.

FIG. 1 depicts a various aspects of an illustrative computingenvironment(s) as well as cognitive automation computer machine(s),platform(s), internal and external computer machine(s), in-network andexternal data source(s)/data store(s), etc., in accordance with one ormore environments, for processing data from disparate sources andpropagating the data to target applications and data stores.

Referring to FIG. 1, computing environment/computing platform(s) 100 mayinclude one or more computer machine(s), system(s), and/or platform(s).For example, computing environment 100 may include various computermachine(s) such as one or more cognitive automation computer machine(s)110, controller and/or controlled computers 115 for distributedprocessing, enterprise data storage platform(s) 120, enterprisecomputing infrastructure 130, enterprise user computing machine(s) 140,administrative computing machine(s) 150, and external computer system(s)160 for remote access and/or processing, or performing any otherfunctions or actions. In addition, client-server arrangements may beimplemented using one or more of the foregoing. For example, thecognitive automation computer machine(s) 110 could be implemented as oneor more server(s) to provide services and functions to one or moreclient machines such as enterprise user computing machine(s) 140,administrative computer machine(s) 150, external computer system(s) 160,one or more other infrastructures, and the like.

As illustrated in greater detail below, each element in computingenvironment 100 may include one or more computing machine(s) andassociated components operating computer software and data configured toperform one or more of the functions described herein. Moreover, thefunctions performed by one machine or platform could be implemented onanother machine or platform in the environment in accordance with one ormore various embodiments of this disclosure. Computing environment 100also includes one or more in-network data sources/data stores 114, 118,and 116, which may contain applications or other computer software anddata. Computing environment 100 may also include one or more externaldata sources/data stores 117, which may also contain applications orother computer software and data. The data stores 114, 116, 117 and 118may contain any type of company information such as data pertaining tosales, inventory, marketing, supplies, calendars, scheduling,accounting, human resource, CRM, ERP, or any other type of data to bemaintained in any type of data store or application. The data stores maybe coupled to and accessible from a network such as, for example, by anetwork-attached storage medium and/or device. Additionally, oralternatively, the data stores 114, 116, 117, and 118 may beimplemented, in whole or in part, internally as part of one or more ofany of cognitive automation computer machine(s) 110, controller and/orcontrolled computers 115 for distributed processing, an enterprise datastorage platform 120, enterprise computing infrastructure 130, anenterprise user computing machine 140, an administrative computingmachine 150, and an external computer system 160.

In addition, and as illustrated in greater detail below, cognitiveautomation computer machine(s) 110, controller and controlled computingmachine(s) 115, enterprise computer infrastructures 130, and enterpriseuser computing machine(s) 140, may be configured to perform variousdistributed processing functions described herein as well as retrieve,parse, segment, process, normalize, store, access, validate, analyze,distribute, enrich, propagate, and/or otherwise act on enterprise orother data. Enterprise computing infrastructure 130 may include one ormore computer machines and/or other computer components. In addition,and as illustrated in greater detail below, enterprise computinginfrastructure 130 may be configured to provide various enterpriseand/or back-office computing functions for an organization, such as afinancial institution. For example, enterprise computing infrastructure130 may include various computer machines and/or computer-executablesoftware and/or data that store and/or otherwise contain accountinformation, such as financial account information including accountbalances, transactions, transaction history, account owner information,and/or other information. In addition, enterprise computinginfrastructure 130 may process and/or otherwise execute transactions onspecific accounts or from various users based on commands and/or otherinformation received from other computer systems included in computingenvironment 100. Additionally, or alternatively, enterprise computinginfrastructure 130 may load data from enterprise data storage platform120 or another data store, manipulate and/or otherwise process suchdata, and return modified data and/or other data to enterprise datastorage platform 120 and/or to other computer machines or systemsincluded in computing environment 100.

Cognitive automation computer machine(s) 110 may be any type of computermachine and may be linked to and/or used by a specific enterprise user(who may, e.g., be an employee, customer, or affiliate of an enterpriseorganization tasked with entering data, updating data, and/orpropagating data). Enterprise user computing device 140 may be any typeof computer machine and may be linked to and/or used by a specificenterprise user (who may, e.g., be an employee or other affiliate of anenterprise organization controlling and/or interacting with controllerand controlled computing device(s) 115 or any other computer machines).Administrative computing device 150 may be any type of computer machineand may be linked to and/or used by an administrative user (who may,e.g., be a network administrator of an enterprise organizationcontrolling and/or interacting with controller and controlled computingdevice(s) 115 or any other computer machines). Enterprise computersystem 160 may be any type of computer machine and may be linked toand/or used by one or more external users (who may, e.g., not beassociated with an enterprise organization controlling and/orinteracting with controller and controlled computing device(s) 115 orany other computer machines).

Computing environment 100 also may include one or more networks, whichmay interconnect one or more of cognitive automation computer machine(s)110, controller and controlled computer machine(s) 115, in-network datasource(s)/store(s) 114, 116, 118, external data source(s)/store(s) 117,enterprise data storage platform 120, enterprise computinginfrastructure 130, enterprise user computing device 140, administrativecomputing device 150, and external computer system 160.

Computing environment 110 may include one or more firewalls 175, whichprotect or filter data for machines, platforms, data and the like,inside the private network from unauthorized users or processesoperating outside the private network.

In one or more arrangements, computer machine(s) and the other system(s)included in computing environment 100 may be any type of computingdevice(s) capable of providing a user interface, receiving input via theuser interface, acting on the input, accessing or processing data,controlling other computer machine(s) and/or component(s) thereof basedon the input, communicating the received input to one or more othercomputing machine(s), and propagating data to other machine(s),platform(s), system(s), data source(s)/data store(s), and the like. Asnoted above, and as illustrated in greater detail below, any and/or allof the computer machine(s) of computer environment 100 may, in someinstances, be special-purpose computing device(s) configured to performspecific functions.

Referring to FIG. 2, one or more computer machine(s) or platform(s),such as, for example, cognitive automation machine 110, may include oneor more processors, executors, cores, etc. 111, memory 112,communication interface 113, and monitor(s) 118. A data bus mayinterconnect processor 111, memory 112, and communication interface 113.Communication interface 113 may be a network interface configured tosupport communication between one or more computer machines in computerenvironment 100 and one or more networks (e.g., private network 170,public network 180, or the like).

Memory 112 may be volatile or non-volatile, and may include computersoftware and data such as, for example, one or more program moduleshaving instructions that when executed by processor 111 cause a computermachine, such as information security computer machine(s) 110, toperform one or more functions described herein and/or access, process,analyze, manipulate, interact with, perform data acquisition, and/orcontrol one or more data stores or data warehouses, big data, or otherfile systems that may store and/or otherwise maintain information whichmay be used by such program modules and/or processor 111. In someinstances, one or more program modules, data, and/or databases may bestored by and/or maintained in different memory units (local oraccessible across the network) of computer machines and/or by differentcomputing devices that may form and/or otherwise make up a collection ofcomputer machines.

Sample program modules, data, and/or databases stored or maintained inmemory may include, but are not limited to: Cognitive AutomationModule(s)—112 a (e.g., for performing general cognitive automationfunctions as known in the industry); Unstructured Data In-TakeModule(s)—112 b (e.g., for ingesting raw data that was manually enteredand/or automatically acquired from digital data in a data source); DataStore(s)/Data Source(s)/Data Warehouse(s)—112 c (e.g., data repositoriesor sources of digital data); Parse Module(s)—112 d (e.g., for parsingunstructured data); Normalization Module(s)—112 e (e.g., for normalizingthe parsed data); Segmentation Module(s)—112 f (e.g., for segmenting thenormalized and parsed data); Internal and/or External SearchModule(s)—112 g (e.g., to facilitate searching and extraction of data);Machine Learning Module(s)—112 h (e.g., code to perform tasks withoutusing explicit instructions such as with supervised learning,semi-supervised learning, and unsupervised learning); Natural LanguageProcessing Module(s)—112 i (e.g., for automatic manipulation andautomatic computational processing of natural language like speech andtext by software); Validation Module(s)—112 j (e.g., for validation ofdata); Remediation Module(s)—112 k (e.g., for making remediations toprocesses and/or data based on human or other corrections to outputs atvarious stages in the cognitive processing); Report GenerationModule(s)—112 l (e.g., to provide reports or logs regarding variousaspects of the cognitive automation process); Distribution/DisseminationModules —112 m (e.g., for distributing the processed data); EnrichmentModule(s)—112 o (e.g., for improving the processed data);Action/Propagation Module(s)—112 p (e.g., to take actions on the dataand/or propagate it based on cognitive automation rules); TransmissionModule(s)—112 l (e.g., for sending data across communication channels ornetwork architectures); Recommendation Module(s)—112 r (e.g., forproviding users with a recommendation in terms of changes to be made orfor additional systems or locations to which data may be propagated suchas through use of a computer prompt or other graphical user interface);Data Entry and Data Population Module(s)—112 s (e.g., for manual entryof data or automatic data population); and/or Target Application/StoreUpdate Module(s)—112 t (e.g., for storing data in target applications ordestinations to obviate the need for repetitive data entry), and/orSpeech Recognition Module(s)—112 u (e.g., to recognize speech andlanguage in digital representations of spoken data such as in recordedtelephone calls or recorded meetings).

Referring to FIG. 3, digital data and information in any documents,files or the like in one data store (e.g., in-network datasource(s)/store(s) 114, 116, 118) can be accessed by cognitiveautomation computer machine(s) as one or more inputs and/or can bewritten to, by the cognitive automation computer machine(s), in order topropagate data from one source to one or more other data source(s)/datastore(s) (e.g., in-network or external data source(s)/store(s) 114, 116,117, and 118). The digital information can be parsed, normalized,segmented, validated, distributed and/or disseminated, as appropriate,in order to facilitate propagation of data to avoid repetitive dataentry.

Referring to FIG. 4, data source(s)/store(s) 114, 116, and 118, andexternal data source(s)/store(s) 117, may contain any type of digitaldata or information stored in isolation and/or across multiple systemsor stores. Sample digital data and/or files includes electronicdocuments 118A (e.g., notes, word processor files, spreadsheets, etc.),audio files 118B (e.g., phone or other recordings), multimedia files118D (e.g., video conferencing recordings), video files 118C (e.g.,moving screen captures/recordings or videotaping), images 118E (e.g.,photographs, still screen captures, etc.), and the like.

There are various scenarios and processes in which it would bebeneficial for a cognitive automation-based engine to propagate dataacross systems having their own data stores in order to maximizeefficiency and avoid time-consuming repetitive data entry in those datastores.

In one or more embodiments, cognitive automation technology is used toidentify information to be propagated without having to have a humanlook at it. All the different interconnected systems where the same datamight be entered can be identified automatically so that a user does nothave to manually identify each system and then enter the same data infive or more different systems. Each of the different systems can beinterconnected together through the cognitive automation tool such thatwhen information is identified or entered once, it can thereafter bepropagated out.

As an example, repetitive data entry in customer relationship management(CRM) and enterprise resource planning (ERP) systems illustrates theneed for the present invention and how the cognitive-based engine incognitive automation machine(s) 110 can overcome the prior art technicalproblems of manual, time consuming, inaccurate, and unproductive entryof data into multiple systems.

For example, in the context of a company's sales orders, thecognitive-based engine of one or more embodiments allows companies toautomatically update, track, and manage customer relationships includingtheir software applications and systems used by customer servicerepresentatives to manage customer interactions, document customerservice processes, and implement a customer-centric philosophy andmethodology, as well as automatically update, track, and manageenterprise resource planning systems including manufacturing, orderingof raw material supplies, engineering, planning, materials management,finance and human resources, and like processes. This is accomplished byintegrating the various systems with one another such that data entryinto any one or more of the foregoing is entered one time, manuallyand/or automatically by machine analysis of a data file in data sourceand/or data store, and then propagated out to all other applicablesystems automatically and/or through a series of manual prompts orsuggestions by the cognitive automation machines that identify all othersystems, data store(s), and/or data source(s) like shown in FIG. 4.

In this context, samples of types of information that could be enteredonce, perhaps in the scenario of a sales order, and then propagated outmanually and/or automatically, include customer names, account numbers,sales orders, purchase orders, invoicing, products ordered, quantity ofproducts ordered, supply sourcing for manufacturing that corresponds tothe orders, inventory, date event tracking, calendar scheduling, salespersonnel, customer service personnel, call logs, etc. Automaticpropagation may include, for example, authentication of potentialupdates; identifying known repositories where the information is knownto reside and/or parsing indices of identifying locations of theapplicable known repositories; searching databases, data stores, anddata warehouses, and the like, based on the identifying information toflag data structures or other files contained therein that need to beupdated; reconciling data entries across disparate systems such that theinformation can be updated; updating (and creating if desired) indicesor other repository references tracking locations of like information;and/or verification of changes to repositories.

As such, in a more detailed example, a customer service representativemight take an order via a recorded telephone call. The order might befor the purchase of five computers. During the call, the salesrepresentative would collect, inter alia, the customer name, telephoneand address information, account number, purchase order number, productsordered, date of the order, shipping method, etc. Once entered, thecognitive automation machine 110 could analyze the data (as detailedbelow) in real-time and then propagate, such as described above, theinformation to, inter alia, one or more systems, applications, datastores or the like in a manufacturing department's computerinfrastructure or the like. The cognitive automation machine 110 and/orthe manufacturing department's infrastructure could then updateapplicable records and/or data repositories to reflect the order,compare the quantity of products ordered and the model of the productordered to its current inventory of finished materials as well as itscurrent inventory of components on hand to make finished materials. Ifnecessary, automatic orders to source or purchase additional componentsnecessary to make the finished products could be updated and placed withsuppliers either by one or more cognitive automation machine(s) 110 orinfrastructure computing devices managing manufacturing processes or thelike. Additionally, cognitive automation machine(s) 110 orinfrastructure computing devices could update and/or populate theshipping information for delivery of the products once assembled in themanufacturing systems data source(s)/data store(s) in order tofacilitate rapid shipping of the computers once assembled and with errorfree shipping addresses automatically having been entered once by thesales representative and propagated to the manufacturing departmentssystems.

This can be accomplished by the cognitive automation-based engine incognitive automation computing machine 110 of one or more embodiments ofthis disclosure such as illustrated in FIG. 5. In particular, cognitiveautomation as used herein is a subset of artificial intelligence—usingspecific AI techniques that mimic that way the human mind works—toassist humans in propagating data including entering data, makingdecisions, completing tasks, or meeting goals. In the context of thepresent disclosure, this means using technologies such as naturallanguage processing, image processing, pattern recognition, andcontextual analyses to make automated and more intuitive leaps,perceptions, and judgments, and automatically learn, discover, and makerecommendations or predictions, all of which obviates the need forrepetitive data entry into multiple systems. In particular, unstructureddata can be used to build relationships and/or otherwise act on orpropagate appropriate information captured from the unstructured data.This allows the cognitive automation engine to determine if somethinghas been seen before and if so, where; what was done in the similarinstance; is it connected to and/or related to something that has beenseen before; what is the strength of the connection; and what wasinvolved. Further, the cognitive automation engine such as in cognitiveautomation machine(s) 110 and/or hosted and executed on serversperforming one or more of the cognitive automation functions can bepretrained to automate specific business processes and data entry, andthus need less data before actions can be taken, avoid requiring helpfrom data scientists and/or IT professionals to build elaborate models.As such, the cognitive automation engine can be implemented in aclient-server architecture or on a standalone machine in one or moreembodiments of this disclosure, and can be used by business ownersthemselves and can be operational in just a few weeks. As new data isadded to the cognitive automation system, the system can make more andmore connections. This allows the cognitive automation system to keeplearning unsupervised, and constantly adjust to the new informationbeing fed and analyzed. Importantly, one or more aspects of thecognitive automation engine of this disclosure can capture data, processit, propagate it, and make decisions in real time.

In another sample scenario in the context of, for example, newinventions invented by inventors, inventors will recognize that theinvention process typically begins with an invention disclosure meetingwith a patent attorney. The patent attorney and all of the inventors mayconduct the disclosure meeting on a telephone conference call.

During the meeting, the patent attorney would likely take copious notesduring the call. The inventors may provide their full name as well astheir residence and citizenship information, which is required as partof formal papers for the patent application. The meeting notes wouldlikely include a list of everyone on the call, their email addresses,the date of the call, the duration of the call, the title of theinvention, a detailed discussion of the inventions, action items tofollow up on, a discussion of due dates, known prior art, etc.

The cognitive automation-based engine of one or more embodiments of thepresent disclosure, such as in cognitive automation computing machine(s)110, other special purpose computers, and/or client-server machines,infrastructure computing devices or the like could take as an input themeeting notes, process the information, and propagate it out to varioussystems thereby obviating the need for repetitive data entry andeliminating the possibility of data entry error.

For example, the cognitive automation-based engine could recognize fromthe meeting notes who the inventors are, their residence, theircitizenship, and the title of the invention. This information could bepropagated out to a database of inventor declarations and a separatedatabase of inventor assignments. The declarations and assignments couldthen be automatically created by the cognitive automation-based enginerather than requiring repetitive manual entry of the information overand over in each separate document.

Similarly, the cognitive automation-based engine could recognize fromthe meeting notes who the inventors are, their email addresses,residential addresses, etc. and could store that information in a seriesof contacts for the inventors and create an email distribution list forthe inventors. Contacts could be stored in a contact database along withemail distribution lists. Automatic reminder emails and follow up emailsto the inventors could be populated and sent from an email database.Such emails may include reminders regarding identification of knownprior art, reminders to provide additional information or drawings,reminders to execute the formal papers, etc. Other automatic emails,such as distribution of the meeting notes, could be circulated as well.

Relatedly, the cognitive automation-based engine could recognize fromthe meeting notes and future follow up meetings that are discussed. Thisinformation could be propagated from the meeting notes to one or morecalendar databases and/or calendar invites could automatically be routedto the email distribution lists.

Also, the cognitive automation-based engine could make suggestion basedon the email notes regarding other databases where some of the relevantinformation may need to be stored or updated. For example, the enginecould recognize that the address or citizenship information for one ofthe inventors has changed and the engine could make suggestions to thepatent attorney to check files for patent applications for other pendingcases to ensure that the new residential and citizenship information isupdated in those attorney files.

By using the cognitive automation-based engine of the one or moreembodiments described herein, repetitive data entry can be avoided.

Referring more specifically to FIG. 5, unstructured data 300 which ismanually entered or automatically acquired from internal and/or externaldata source(s)/data store(s) 114, 115, 116, and 117 can be used as inputto cognitive automation computer machine(s) 110, cognitive automationprocesses implemented in various client-server architectures, and/ornetwork or system infrastructures, a cognitive automation system, and/orbe included in one or more of such system(s).

As an example, in the context of cognitive automation machine(s) 110 (orimplemented in any of the like as suggested above), unstructured data300 is parsed in step 302 by cognitive automation computing machine(s)110. In particular, the unstructured data 300 is processed intocomponents by cognitive automation computing machine(s) 110, which thenanalyze the data 300 for correct syntax and then attached by cognitiveautomation computing machine(s) 110 to tags that define each component.The cognitive automation system (110 or the like) can then process eachportion of the data and transform it into machine language if desired.As part of the parsing step, ordinary text can be broken up and used bycognitive automation computing machine(s) 110. For example, searchengines typically parse search phrases entered by users so that they canmore accurately search for each word. And, the cognitive automationsystem can parse text documents and extract certain information likenames or addresses. Moreover, in the example of a spreadsheet, thecognitive automation system can turn formatted documents into tableswith rows and columns by parsing the text.

Next, in step 304, the parsed information can be normalized by cognitiveautomation computing machine(s) 110. This is a process that breaks downdata into record groups for efficient processing. There are typicallysix stages. By the third stage (third normal form), data are identifiedonly by the key field in their record by cognitive automation computingmachine(s) 110. For example, ordering information is identified bycognitive automation computing machine(s) 110 by order number, andcustomer information by customer number. A major goal of normalizationby cognitive automation computing machine(s) 110 is to eliminateredundancy by having a data element represented in only one place. Inthe context of a database, the normalization by cognitive automationcomputing machine(s) 110 can involve dividing data into two or moretables and defining relationships between the tables. The objective forthe cognitive automation computing machine(s) 110 is to isolate data sothat additions, deletions, and modifications of a field can be made injust one table and then propagated by cognitive automation computingmachine(s) 110 through the rest of the database via the definedrelationships.

Typically, there are there are three main normal forms, each withincreasing levels of normalization. The first is a first normal form(1NF), wherein each field in a table contains different information. Forexample, in an employee list, each table would contain only onebirthdate field. In the second normal form (2NF), each field in a tablethat is not a determiner of the contents of another field must itself bea function of the other fields in the table. In the third normal form(3NF), no duplicate information is permitted. So, for example, if twotables both require a birthdate field, the birthdate information wouldbe separated into a separate table, and the two other tables would thenaccess the birthdate information via an index field in the birthdatetable. Any change to a birthdate would automatically be reflect in alltables that link to the birthdate table. Further, additionalnormalization levels can be utilized such as the Boyce Codd Normal Form(BCNF), fourth normal form (4NF) and fifth normal form (5NF). Whilenormalization makes databases more efficient to maintain, they can alsomake them more complex because data is separated into so many differenttables. Additionally and/or alternatively, the normalization may beconsidered as a process applied by cognitive automation computingmachine(s) 110 to all data in a set that produces a specific statisticalproperty.

Next, in step 306, the normalized data can be segmented by cognitiveautomation computing machine(s) 110. This refers to the breaking ofpackets, by cognitive automation computing machine(s) 110, into smallerpieces in order to overcome the restrictions of network bandwidth and/orlimitations in the communication channels. Applicable portions of thesegmented data can be joined back together at receivers oncedistribution and/or dissemination is complete in step 308.

Additionally and/or alternatively, the segmented data can be validatedin step 310 by cognitive automation computing machine(s) 110 eitherprior to or immediately after distribution and dissemination in step308. The validation can include one or more functions performed bycognitive automation computing machine(s) 110 such as, for example: datavalidation by ensuring that the data satisfies defined formats and otherinput criteria; forecast verification by validating and verifyingprognostic output from numerical models; regression validation bydetermining whether the outputs of a regression model are adequate;social validation to verify compliance in a social activity to fit inand be part of the majority; statistical model validation by determiningwhether the outputs of a statistical model are acceptable; documentingthat a process or system meets its predetermined specifications andquality attributes; checking that the segmented and normalized datameets specifications and fulfills its intended purpose; and/or checkingthe data to confirm that it both is well-formed and follows a definedstructure.

After validation in step 310 and as a result ofdistribution/dissemination in step 308, the segmented, validated, andnormalized data can be received as in step 312 by systems, datastore(s), and/or data source(s) to be updated, thereby obviating theneed for repetitive data entry.

In step 314, the received data can be enriched by cognitive automationcomputing machine(s) 110. As used herein, data enrichment refers toprocesses, performed by cognitive automation computing machine(s) 110,that are used to enhance, refine or otherwise improve the received data.This idea and other similar concepts contribute to making data avaluable asset for almost any modern business or enterprise. It alsoshows the common imperative of proactively using this data in variousways. Although data enrichment can work in many different ways, many ofthe tools used for this goal involve a refinement of data, by cognitiveautomation computing machine(s) 110, that might include small errors. Acommon data enrichment process, implemented by cognitive automationcomputing machine(s) 110, could, for example, correct likelymisspellings or typographical errors in a database through the use ofprecision algorithms. Following this logic, data enrichment tools couldalso add information to simple data tables. Another way that dataenrichment can work, as used herein, is in extrapolating data. Throughmethodologies such as fuzzy logic, more information can be produced froma given raw data set.

Relatedly, based on data enrichments in step 314, recommendations may bemade by the cognitive-automation system 110 to make recommendations in316. For example, if clear relationships between data that is enteredand fields in target locations are not previously established, thecognitive-automation system can make recommendations to users to suggestadditional locations and/or system(s) in which the data may be useful.This could be accomplished, for example, by a series of prompts providedto the users by cognitive automation computing machine(s) 110. And, thecognitive-automation system could learn from each manual interventionand then make automatic decisions in the future based on the historicaldecisions.

After the data is enriched in step 314 and/or any recommendations aremade by the cognitive-automation system, the data may be acted on and/orpropagated in step 318, by cognitive automation computing machine(s)110, to various target applications, systems, databases, data sources,data stores, or the like in step 320. If desired, optional automationreporting as in step 322 by cognitive automation computing machine(s)110 can generate logs or similar reports for operator or systemadministrator review and further cognitive-automation refinements.

One or more aspects of the disclosure may be embodied in computer-usabledata or computer-executable software or instructions, such as in one ormore program modules, executed by one or more computers or other devicesto perform the operations described herein. Generally, program modulesinclude routines, programs, objects, components, data structures, andthe like that perform particular tasks or implement particular abstractdata types when executed by one or more processors in a computer orother data processing device. The computer-executable instructions maybe stored as computer-readable instructions on a computer-readablemedium such as a hard disk, optical disk, removable storage media,solid-state memory, RAM, and the like. The functionality of the programmodules may be combined or distributed as desired in variousembodiments. In addition, the functionality may be embodied in whole orin part in firmware or hardware equivalents, such as integratedcircuits, application-specific integrated circuits (ASICs), fieldprogrammable gate arrays (FPGA), and the like. Particular datastructures may be used to more effectively implement one or more aspectsof the disclosure, and such data structures are contemplated to bewithin the scope of computer-executable instructions and computer-usabledata described herein.

Various aspects described herein may be embodied as a method, anapparatus, or as one or more computer-readable media storingcomputer-executable instructions. Accordingly, those aspects may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, an entirely firmware embodiment, or an embodiment combiningsoftware, hardware, and firmware aspects in any combination. Inaddition, various signals representing data or events as describedherein may be transferred between a source and a destination in the formof light or electromagnetic waves traveling through signal-conductingmedia such as metal wires, optical fibers, or wireless transmissionmedia (e.g., air or space). In general, the one or morecomputer-readable media may be and/or include one or more non-transitorycomputer-readable media.

As described herein, the various methods and acts may be operativeacross one or more computing servers, computing platforms, and/or one ormore networks. The functionality may be distributed in any manner or maybe located in a single computing device (e.g., a server, a clientcomputer, and the like). For example, in alternative embodiments, one ormore of the computing platforms discussed above may be combined into asingle computing platform, and the various functions of each computingplatform may be performed by the single computing platform. In sucharrangements, any and/or all of the above-discussed communicationsbetween computing platforms may correspond to data being accessed,moved, modified, updated, and/or otherwise used by the single computingplatform. Additionally, or alternatively, one or more of the computingplatforms discussed above may be implemented in one or more virtualmachines that are provided by one or more physical computing devices. Insuch arrangements, the various functions of each computing platform maybe performed by the one or more virtual machines, and any and/or all ofthe above-discussed communications between computing platforms maycorrespond to data being accessed, moved, modified, updated, and/orotherwise used by the one or more virtual machines.

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one or more of the steps depicted in theillustrative figures may be performed in other than the recited order,and one or more depicted steps may be optional in accordance withaspects of the disclosure.

What is claimed is:
 1. A cognitive automation-based method forpropagating data across multiple systems via a private network to avoidrepetitive data entry by a user, the method comprising the steps of: a.receiving, by a cognitive automation platform from a data source, inputsource data; b. storing, by the cognitive automation platform in a firstsector of computer-readable media, the input source data; c. extractingin real-time, by the cognitive automation platform from the input sourcedata in the first sector of the computer-readable media, data to bepropagated; d. storing in real-time, by the cognitive automationplatform in a second sector of the computer-readable media, the data tobe propagated; e. parsing in real-time, by the cognitive automationplatform, the data to be propagated into parsed data; f. storing inreal-time, by the cognitive automation platform in a fourth sector ofthe computer-readable media, the parsed data; g. normalizing inreal-time, by the cognitive automation platform, the parsed data intonormalized data for efficient processing, said normalized datanormalized into at least a first normal form; h. storing in real-time,by the cognitive automation platform in a fifth sector of thecomputer-readable media, the normalized data; i. segmenting inreal-time, by the cognitive automation platform, the normalized datainto segmented data by breaking the normalized data into a plurality ofsmaller packets for efficient data transmission; j. storing inreal-time, by the cognitive automation platform in a sixth sector of thecomputer-readable media, the segmented data; k. validating in real-time,by the cognitive automation platform, the segmented data into validateddata to ensure that the validated data satisfies defined formats andinput criteria; l. storing in real-time, by the cognitive automationplatform in a seventh sector of the computer-readable media, thevalidated data; and m. distributing in real-time, by the cognitiveautomation platform over the private network, the validated data into aplurality of data stores communicatively coupled to the private network,thereby propagating said validated data without repetitive data entry.2. The cognitive automation-based method of claim 1 further comprisingthe steps of: a. enriching in real-time, by the cognitive automationplatform, the validated data to enriched data that is corrected for anyerrors; and b. storing in real-time, by the cognitive automationplatform in an eighth sector of the computer-readable media, theenriched data.
 3. The cognitive automation-based method of claim 2further comprising the steps of: a. generating in real-time, by thecognitive automation platform, a recommendation of at least oneadditional target in which the enriched data may be stored; b. storingin real-time, by the cognitive automation platform in a ninth sector ofthe computer-readable media, the recommendation of said at least oneadditional target in which the enriched data may be stored; c.transmitting in real-time, by the cognitive automation platform over theprivate network, the recommendation; and d. storing, by the cognitiveautomation platform, the enriched data in the at least one additionaltarget if approved.
 4. The cognitive automation-based method of claim 3wherein the data to be propagated into said parsed data by analyzing thedata to be propagated for correct syntax.
 5. The cognitiveautomation-based method of claim 4 wherein the parsed data is normalizedby breaking the parsed data into record groups for efficient processing.6. The cognitive automation-based method of claim 5 wherein the inputsource data is a digital file.
 7. The cognitive automation-based methodof claim 5 wherein the digital file comprises unstructured data.
 8. Thecognitive automation-based method of claim 6 wherein the input sourcedata is generated by scanning the digital file.
 9. The cognitiveautomation-based method of claim 8 wherein the validation of thesegmented data into said validated data is validated by forecastvalidation based on prognostic output from at least one numerical model.10. The cognitive automation-based method of claim 8 wherein thevalidation of the segmented data into said validated data is validatedby regression validation by determine whether an output of a regressionmodel is adequate.
 11. The cognitive automation-based method of claim 8wherein the validation of the segmented data into said validated data isvalidated by statistical model validation to determine with outputs of astatistical model are acceptable.
 12. The cognitive automation-basedmethod of claim 8 wherein the validation of the segmented data into saidvalidated data is validated by documenting that a process meetspredetermined specifications and fulfills an intended purpose.
 13. Thecognitive automation-based method of claim 8 wherein the validation ofthe segmented data into said validated data is validated by checkingwhether segmented data follows a defined structure.
 14. A cognitiveautomation-based method for propagating data across multiple systems viaa private network to avoid repetitive data entry by a user, the methodcomprising the steps of: a. receiving, by a cognitive automationplatform from a data source, input source data; b. storing, by thecognitive automation platform in a first sector of computer-readablemedia, the input source data; c. extracting in real-time, by thecognitive automation platform from the input source data in the firstsector of the computer-readable media, data to be propagated; d. storingin real-time, by the cognitive automation platform in a second sector ofthe computer-readable media, the data to be propagated; e. parsing inreal-time, by the cognitive automation platform, the data to bepropagated into parsed data; f. storing in real-time, by the cognitiveautomation platform in a fourth sector of the computer-readable media,the parsed data; g. normalizing in real-time, by the cognitiveautomation platform, the parsed data into normalized data for efficientprocessing, said normalized data normalized into at least a first normalform; h. storing in real-time, by the cognitive automation platform in afifth sector of the computer-readable media, the normalized data; i.segmenting in real-time, by the cognitive automation platform, thenormalized data into segmented data by breaking the normalized data intoa plurality of smaller packets for efficient data transmission; j.storing in real-time, by the cognitive automation platform in a sixthsector of the computer-readable media, the segmented data; k. validatingin real-time, by the cognitive automation platform, the segmented datainto validated data to ensure that the validated data satisfies definedformats and input criteria; l. storing in real-time, by the cognitiveautomation platform in a seventh sector of the computer-readable media,the validated data; m. enriching in real-time, by the cognitiveautomation platform, the validated data to enriched data that iscorrected for any errors; and n. storing in real-time, by the cognitiveautomation platform in an eighth sector of the computer-readable media,the enriched data.
 15. The cognitive automation-based method of claim 14further comprising the steps of: a. generating in real-time, by thecognitive automation platform, a recommendation of at least oneadditional target in which the enriched data may be stored; b. storingin real-time, by the cognitive automation platform in a ninth sector ofthe computer-readable media, the recommendation of said at least oneadditional target in which the enriched data may be stored; c.transmitting in real-time, by the cognitive automation platform over theprivate network, the recommendation; and d. storing, by the cognitiveautomation platform, the enriched data in the at least one additionaltarget if approved.
 16. The cognitive automation-based method of claim15 further comprising the step of distributing in real-time, by thecognitive automation platform over the private network, the validateddata into a plurality of data stores communicatively coupled to theprivate network, thereby propagating said validated data withoutrepetitive data entry.
 17. The cognitive automation-based method ofclaim 16 wherein the data to be propagated into said parsed data byanalyzing the data to be propagated for correct syntax.
 18. Thecognitive automation-based method of claim 17 wherein the parsed data isnormalized by breaking the parsed data into record groups for efficientprocessing.
 19. A cognitive automation-based method for propagating dataacross multiple systems via a private network to avoid repetitive dataentry by a user, the method comprising the steps of: a. receiving, by acognitive automation platform from a data source, input source data; b.storing, by the cognitive automation platform in a first sector ofcomputer-readable media, the input source data; c. extracting inreal-time, by the cognitive automation platform from the input sourcedata in the first sector of the computer-readable media, data to bepropagated; d. storing in real-time, by the cognitive automationplatform in a second sector of the computer-readable media, the data tobe propagated; e. parsing in real-time, by the cognitive automationplatform, the data to be propagated into parsed data; f. storing inreal-time, by the cognitive automation platform in a fourth sector ofthe computer-readable media, the parsed data; g. normalizing inreal-time, by the cognitive automation platform, the parsed data intonormalized data for efficient processing, said normalized datanormalized into at least a first normal form; h. storing in real-time,by the cognitive automation platform in a fifth sector of thecomputer-readable media, the normalized data; i. segmenting inreal-time, by the cognitive automation platform, the normalized datainto segmented data by breaking the normalized data into a plurality ofsmaller packets for efficient data transmission; j. storing inreal-time, by the cognitive automation platform in a sixth sector of thecomputer-readable media, the segmented data; k. validating in real-time,by the cognitive automation platform, the segmented data into validateddata to ensure that the validated data satisfies defined formats andinput criteria; l. storing in real-time, by the cognitive automationplatform in a seventh sector of the computer-readable media, thevalidated data; m. generating in real-time, by the cognitive automationplatform, a recommendation of at least one additional target in whichthe validated data may be stored; n. storing in real-time, by thecognitive automation platform in an eighth sector of thecomputer-readable media, the recommendation of said at least oneadditional target in which the enriched data may be stored; o.transmitting in real-time, by the cognitive automation platform over theprivate network, the recommendation; and p. storing, by the cognitiveautomation platform, the enriched data in the at least one additionaltarget if approved.
 20. The cognitive automation-based method of claim19 wherein the data to be propagated into said parsed data by analyzingthe data to be propagated for correct syntax and the parsed data isnormalized by breaking the parsed data into record groups for efficientprocessing.